Haddix Chase, Bates Madison, Garcia-Pava Sarah, Salmon Powell Elizabeth, Sawaki Lumy, Sunderam Sridhar
F. Joseph Halcomb III, MD, Department of Biomedical Engineering, University of Kentucky, Lexington, KY 40506, United States of America.
Universities Space Research Association, Cleveland, OH, United States of America.
Biomed Phys Eng Express. 2025 Feb 7;11(2). doi: 10.1088/2057-1976/adabeb.
Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls. Participants extended their fingers to one of four levels: low, medium, high, or 'no-go' (none), while hand, muscle (electromyography: EMG), and brain (EEG) activity were monitored. Event-related desynchronization (ERD) was measured as the change in 8-30 Hz EEG power during movement. Classifiers were trained on EEG features, EMG power, or both (EEG+EMG) to decode finger extension, and accuracy assessed via four-fold cross-validation for each hand of each participant. Mean accuracy exceeded chance (25%) for controls (n = 11) at 62% for EMG, 60% for EEG, and 71% for EEG+EMG on the left hand; and 67%, 60%, and 74%, respectively, on the right hand. Accuracies were similar on the unimpaired right hand for the stroke group (n = 3): 61%, 68%, and 78%, respectively. But on the paretic left hand, EMG only discriminated no-go from movement above chance (41%); in contrast, EEG gave 65% accuracy (68% for EEG+EMG), comparable to the non-paretic hand. The median ERD was significant (p < 0.01) over the cortical hand area in both groups and increased with each level of finger extension. But while the ERD favored the hemisphere contralateral to the active hand as expected, it was ipsilateral for the left hand of stroke due to the lesion in the right hemisphere, which may explain its discriminative ability. Hence, the ERD captures effort in finger extension regardless of success or failure at the task; and harnessing residual EMG improves the correlation. This marker could be leveraged in rehabilitative protocols that focus on fine motor control.
脑机接口（BCIs）为残疾人士提供了通过解读脑电图（EEG）与设备进行交互的手段。然而，在精细运动任务中解读意图可能具有挑战性，尤其是对于患有皮质病变的中风幸存者而言。在此，我们尝试从患有左手轻瘫的中风患者及健康对照者的脑电图中解读分级手指伸展动作。参与者将手指伸展至四个级别之一：低、中、高或“不做动作”（无动作），同时对手部、肌肉（肌电图：EMG）和脑部（EEG）活动进行监测。事件相关去同步化（ERD）被测量为运动期间8 - 30赫兹脑电图功率的变化。分类器基于脑电图特征、肌电图功率或两者（EEG + EMG）进行训练以解读手指伸展动作，并通过对每位参与者每只手进行四折交叉验证来评估准确性。对于对照组（n = 11），左手的肌电图平均准确率超过机遇水平（25%），为62%；脑电图为60%；EEG + EMG为71%；右手分别为67%、60%和74%。中风组（n = 3）未受损右手的准确率与之相似：分别为61%、68%和78%。但在患侧左手，肌电图仅能以高于机遇水平（41%）区分不做动作与有动作；相比之下，脑电图的准确率为65%（EEG + EMG为68%），与未患侧手相当。两组在皮质手部区域的ERD中位数均具有显著性（p < 0.01），且随着手指伸展级别升高而增加。但是，虽然ERD如预期那样偏向于活动手对侧的半球，但由于右半球存在病变，中风患者左手的ERD偏向同侧，这可能解释了其辨别能力。因此，无论任务成功与否，ERD都能反映手指伸展时的努力程度；利用残余肌电图可提高相关性。这一指标可应用于专注于精细运动控制的康复方案中。
